Most papers by Jon Bentley (e.g. A Sample of Brilliance) are also great reads.
I'm a frequent contributor to Fermat's Library, which posts an annotated paper (CS, Math and Physics mainly) every week. If you are looking for interesting papers to read, I would strongly recommend checking it out - http://fermatslibrary.com/
- Reflections on Trusting Trust (Annotated Version) - http://fermatslibrary.com/s/reflections-on-trusting-trust
- A Sample of Brilliance (Annotated Version) - http://fermatslibrary.com/s/a-sample-of-brilliance
So point is, it's been done. That's why the Equation Group malware operated undetected for almost a decade (and maybe longer than that). Never underestimate the power of a government -- they can afford to hire and train an army of Ken Thompsons for the price of an aircraft carrier.
Where can I read more technical details (such as code analysis) about this?
I've never heard of anything like this hiding for 10 years before.
The un-named nls_933w.dll responsible for modifying firmware on "over a dozen" HDDs is only termed as Stuxnet-like however: https://securelist.com/equation-the-death-star-of-malware-ga...
That's remarkably impressive though.
HDDs aren't impenetrable walls - http://spritesmods.com/?art=hddhack - but reflashing firmware on over a dozen disks, presumably from Windows... nice.
Sure, it costs billions. But to a nation-state, billions are easy to find.
This is why modern security tools and practices are really only going to be capable of keeping out criminal organizations and mass-hacks. If a nation-state decides to target you, there is really no way you can defend against it. Often they are able to undermine the trust mechanisms in place through sheer resource asymmetry (they have the compute resources to brute-force SSL key collisions -- they did this with Stuxnet to fake a Microsoft signing cert to push the payload via a MITMed Windows Update).
There are even reports of three-letter-agencies intercepting routers during shipment, desolderig chips from the board, and replacing them with "bugged" chips containing back doors in hardware; then packaging it all up and getting it delivered on-time.
You just can't fight that kind of power; even as a company as large as Google or Apple. Nation states will always be able to probe and exploit the edge cases in your security model. In general, you can't make anything totally secure, but you can try to make it cost enough to break into that it will deter anyone who can't justify the cost.
It was really ingenious in a lot of ways: it targeted a specific industrial controller card. Even then, all it did was use the controller card to introduce a subtle voltage fluctuation in the power supply in 1/10 of the centrifuges that rapidly burned out the motors.
Basically, it introduced subtle errors into the system that the Iranians spent about a year trying to resolve. It also spread itself through some ingenious mechanisms to avoid air gaps -- in this case it is suspected they infiltrated a supplier for the centrifuges in China via spear phishing and got it on a USB drive from the supplier to cross the air gap (the way it embeds and hides itself in USB microcode is pretty cool).
The whole story reads like a spy novel; except it actually happened. It's one of my favorite examples of how a nation-state can use cyberterrorism to sabotage an enemy from the shadows -- and this action saved lives, because the alternative was an Israeli air strike on the compound. IMO this is a great example of ethical super-spy hacking.
If you have two compilers and one is open source (and you've read the source and happy that it's clean), you can compile that source with both compilers. The output will be different because the two compilers will make different optimizations. However, now you have two binaries of the same compiler and while they aren't the same, their output will be. So you can re-compile the source with both new binaries and you should get a bit-for-bit equivalent output.
Others in this thread have already mentioned papers or opinionated essays that quickly came to mind, including "Reflections on Trusting Trust" by Ken Thompson, "A Mathematical Theory of Communication" by Claude Shannon (incredibly well-written and easy-to-follow given the subject matter), and "Recursive Functions of Symbolic Expressions and Their Computation by Machine" by John McCarthy.
I would also mention "On Computable Numbers, with an Application to the Entscheidungsproblem" by Alan Turing, "On Formally Undecidable Propositions of Principia Mathematica And Related Systems" by Kurt Gödel, and "The Complexity of Theorem Proving Procedures" by Stephen Cook, but in my view these papers are 'unnecessarily' challenging or time-consuming to read, to the point that I think it's better to read textbooks (or popular works like "Gödel, Escher, and Bach" by Douglas Hofstadter) covering the same topics instead of the original papers. Still, these papers are foundational.
Finally, I think "The Mythical Man-Month" by Fred Brooks, and "Worse is Better" by Richard Gabriel merit inclusion here, given their influence.
This is by no means an exhaustive list. Many -- many -- other worthy papers will surely come to mind over the course of the day that I won't have a chance to mention here.
There are many other good recommendations elsewhere in this thread, including papers/essays I have not yet read :-)
Fyi, the original paper by Ralph Merkle where he introduces what is later called Merkle Trees is definitely worth knowing. I wouldn't say it is my fav, but it resulted in a key component in some of my favorite technologies.
An amazing contrast: "Publish or perish" driven research vs "I don't want the fame, I just want to build something useful and practical".
Time, Clocks, and the Ordering of Events in a Distributed System. Leslie Lamport.
My first introduction to time scales as a partial ordering. Very mind opening.
I once spoke to Henri Gouraud after he gave a talk. He was very self-deprecating and acutely embarrassed that his name was attached to a blindingly obvious first-thing-that-comes-into-your-head shading expression-that-barely-deserves-the-name-algorithm. Sometimes that low hanging stuff gives you stomach ache.
"Designing croquet's TeaTime: a real-time, temporal environment for active object cooperation", David Reed :
base 2 is used the resulting units may be called binary digits, or more briefly bits, a word suggested by
J. W. Tukey"
The first half of the paper is a spot-on critique of so many things that go wrong in the process of designing and implementing large-scale software systems. The second half, where the authors propose a solution, kind of goes off the rails a bit into impracticality... but they definitely point in a promising direction, even if nobody ever uses their concrete suggestions.
“…programming properly should be regarded as an activity by which the programmers form or achieve a certain kind of insight, a theory, of the matters at hand. This suggestion is in contrast to what appears to be a more common notion, that programming should be regarded as a production of a program and certain other texts.”
One of them, as I understand it, is that in any significant software project, regardless of the volume and quality of the documentation, or quality of the code base, maintainers not involved in building the original project will not be able to build the "theory" correctly in their minds, and will consequently make changes that are clumsy or detrimental. (I'm summarizing, so some important aspects have been skipped).
I see this aspect of the paper as related to the "conceptual integrity" discussed in the Mythical Man-Month.
This paper has long been one of my favorites, and was first brought to my attention when I was reading (IIRC) one of Alistair Cockburn's books. Sadly, few of the people I shared it with found it interesting.
"Generic Top-down Discrimination for Sorting and Partitioning in Linear Time" ->
(if you're daunted by an 80 page paper as I am, there is also a talk on it: https://www.youtube.com/watch?v=sz9ZlZIRDAg)
It is possible, with some proper insight and approaches, to sort general datastructures in linear time on modern computing hardware. The speed limit of sort is O(n) with some extra constant cost (often accrued by allocation). It works by decomposing and generalizing something akin to radix sort, leveraging a composable pass of linear discriminators to do the work.
There's a followup paper using this to make a very efficient in-memory database that one could easily generalize under something like kademelia and with care I suspect could make something like a better spark core.
I keep submitting and talking about this but no one seems to pick up on it. This paper is crazy important and every runtime environment SHOULD be scrambling to get this entire approach well-integrated into their stdlib.
Unsurprisingly, Kmett has already implemented it in Haskell (it generalized neatly under the dual of the applicative+alternative functor): https://hackage.haskell.org/package/discrimination
In particular, I generalised discrimination (and added a touch of linear algebra) to devise a simple multi-way join algorithm that computes the join of any number of relations in optimal time (in a specific technical sense). Such results have been obtained recently, but only with far more complicated algorithms.
Alas, the fully general proof of optimality eludes us, so nothing has been published yet.
The case for triangle joins is simple to describe, though. Suppose we have attributes A, B and C and finite maps (relations) R : A x B -> X, S : A x C -> Y and T : B x C -> Z. Now build tries for each so we have
abx : Trie A (Trie B X)
acy : Trie A (Trie C Y)
bcz : Trie B (Trie C Z)
We can join two tries using join : Trie k v1 -> Trie k v2 -> (v1 -> v2 -> v) -> Trie k v. Putting this all together we have:
triangleJoin : Trie A (Trie B (Trie C (X, Y, Z)))
triangleJoin = join abx acy (\bx cy -> join bx bcz (\x cz -> join cy cz (\y z -> (x, y, z))))
Compared to a database that doesn't know how to handle triangle joins the difference is absolutely stunning. The above one-liner managed to do in 5 seconds what took MySQL over an hour...
I've been working on a project that sits somewhere between spark and riak and this actually might address a core issue I have both at the node and query dispatch level.
The succinct version is that all (discrete) data can be serialised as bit strings and those bit strings can be sorted in linear time by (say) radix sort.
There are two ideas at work here: that all (discrete) data structures ultimately are composed of primitive types that can be sorted in linear time, and that we should only examine each primitive value once.
However, to be fair it should be mentioned that the analysis uses a different machine model (the RAM model) than is usually employed in sorting (where all comparisons are considered to take constant time, which is arguably an inferior model of real computers if you are sorting e.g. strings of variable length).
To be honest, though, the original paper is so well-written that I have a hard time explaining it better.
>Sadly, it seems like the HN crowd won't upvote a paper that's the CS equivalent of breaking the speed of light:
Comments like this will turn people off the rest of your post.
Since AP CompSci in HS it's been hammered into students that any sort based on comparisons has a strict lower bound of O(n*log n).
And sorting is particularly important in search engines, of which I've been working on.
So, an algorithm that drastically improves the speed of sorting would actually open up a few more possibilities to consider.
Thanks for sharing the paper KirinDave, I plan to read it.
I dunno how I "persuade" more.
In your original post, the first paragraph made you sound like a crank. You said the paper is like breaking the speed of light, but you don't even mention the topic of the paper. I almost stopped reading at that point. Similarly for the sentence towards the end saying that this is "crazy important". People can decide that for themselves. They can decide even better if you put stuff into context.
So to persuade more, for this specific post of yours, I would have suggested to replace the first paragraph by something like: "Here is a surprising paper that shows that you can sort general data structures in linear time!"
I literally mention the TITLE of the paper, itself precisely explaining its domain & method, directly after the statement and end it with a semicolon.
Let's be real here. The paper is a real dragon of a read. If you're not going to go past a single surprising sentence maybe it was pointless for me to mention it anyways.
> "Here is a surprising paper that shows that you can sort general data structures in linear time!"
I have done this twice, tried different tact twice more, and been downvoted or ignored every time. This is the new me, assuming that folks just don't know how their every artifice of computation is backed by sort. And I suppose... why would they? A great many people simply skip even the basic theory of computation as they join the industry now, and maybe that's okay.
But I say it precisely as I do to generate shock. It should be surprising. I've caught people interviewing for principal engineering positions at Google and Amazon off guard with this. It's very, very surprising.
You should have tried this: "This weird trick by a dad sorts in linear time. Check it out!" Proven to work on so many ad-ridden clickbait websites, so why shouldn't it work on HN? ;-)
The short comment "weird trick" that you replied to was just a joke intending to yield a smile to readers, including you.
Actually I liked the "speed limit" way you previously used to submit the paper on HN.
That said, I skimmed through the article you mention and found it to look serious and instructive (from my experience getting a Ph.D. in computer science / robotics, yet nothing does not guarantee anything) yet needing to allocate a serious time slot for actual understanding. Many people, even on HN, don't upvote due to complexity, yet it was right to submit it.
A number of other insightful comments were written in this thread, thanks for them. Also, your ELI5 explanations are interesting.
My current feeling is like: this sort/discriminator stuff is probably valuable, though it will start usage in demanding situations. It may also eventually be used, without their users even knowing, as a private implementation detail of some data structure in high-level languages. Wait and see.
Back to feelings, this planet has some drawbacks but all in all it's worth it. B612 is too small, we're better here. You can expect good things from HN an similar communities but don't expect too much. Try to refrain from complaining, this feeds the negative part of you and readers as human nature tends to stick bad karma to the ones who complain. Also, when disappointed try to not misattribute causes and favor doubt. Feed the positive part of life.
I can't bring myself to clickbait genuine science and math.
Try to write a blog post on Medium that breaks it down and submit it that way.
I think it's weird that the entire industry is not burning a hole in the atmosphere as they run to implement this wherever they can. It's a very big deal.
I suspect the problem is the paper is 80 pages. But I did link to a youtube talk that covers all the core features.
Also, you really improve your forgetting a word skills.
The methodology isn't _really_ new, but it isn't used frequently. While they showed it as competitive to commonly used sorting algorithms, situationally it can really shine and show significant performance benefits. I'm surprised they didn't show this in one of the graphs (or I missed it in my brief speed-thru).
I don't really see a future for this in standard libraries. I can totally see this methodology being used in the wild in a variety of systems where time = money or entities are looking for a small proprietary distinguishing edge.
I'm not sure why stdlibs for some languages shouldn't take this approach though. It's difficult, sure, but so is any new foundational tech. What do you see as the barrier?
I can totally see it as part of any number of extended libraries.
Just my opinion though. I would be happy to be wrong.
I like his system for generating these lists when there's nested data. However, it's often as simple as concatenating the lists generated by the fields of the object.
The O(n) time bound isn't surprising to me, since each byte of information in the input can lead to at most one scan through the bucket array. Actual performance is obviously a lot better.
I'd still like to see this integrated into a more mainstream language. It would be an interface where all you have to implement is a generator that concatenates the generators of the fields of your class in the desired order.
I know how to do any combination of fixed-width numbers, structs, unions, strings, but I'm not sure how to handle tree-like structures, and something with cycles... I can't even think of a regular comparator for.
I assume this isn't this simple, so what am I missing?
There is a well-known proof from information theory that the problem of sorting n distinct items has a worst-case time complexity of Ω(n log n) fixed-bitwidth operations: (1) Since each of the n items is distinct, the average number of bits needed to encode each item is Ω(log n). (2) In the worst case, in order to correctly sort the items, each bit of each item needs to be read. (3) Therefore, sorting the list requires reading Ω(n log n) bits.
So, I'm not sure how to understand the claim that their algorithm operates in "linear time". Are they saying O(n) operations where each operation operates on O(log n) bits?
[Edit: See below response from kraghen: The time is linear in the total size of the data, not in the number of items. I'll leave this comment up in case anyone has the same misunderstanding that I had.]
(I wasn't, by the way, only considering strings when discussing variable sized keys -- the beauty of discrimination is that we essentially reduce all sorting problems to string sorting.)
I note on slide 14 of this presentation, it looks like this is sort of the discriminator for selecting a better partitioning scheme. So it looks to me like this actually leverages a similar principle?
As we've seen in this thread and others, there are some other ways to measure and/or process that have different characteristics. Surely all of these deserve attention! So, thanks very much for sharing this.
Multikey Quicksort indeed looks like a special case of discrimination, exploiting some of the same principles.
Remember that this proof is talking in terms of the number of comparisons necessary to sort n items. The moment you stop comparing data, like in radix sort (or more generally, bucket sort), that all flies out the window.
Yes, as in any other claimed complexity about an algorithm. According to your proof finding the maximum in a list is Ω(n log n), which isn't the commonly used measure.
Now, take the rational numbers. For the usual binary comparison we can simply define (a/b) < (c/d) as ad < cb. It's not obvious how to express this as a reduction to a primitive ordering (the answer is to compute the continued fraction).
In fact, I'm not aware of any systematic way of deriving discrimination-suitable orderings from binary predicates -- it might be an open research problem, as far as I am aware.
That'd be an even more remarkable discovery in light of the stuff you worked on though, wouldn't it?
The other catch is that no one has demonstrated that you can do it without a good static type system. It shouldn't be impossible, but there are a lot of challenges in an already challenging algorithm.
So, you take this deck of cards, which is in any old order, as the king just played with it last night, and you set about sorting it. Now, you've been doing this a long time, and your methodology has improved over the years. When you started, you would just lay the cards out in the order the king gave them to you, and then start at the beginning of the line and ask "is this card greater than the next?" and move the two cards in response to the answer so that the two are in order. You would continue this process, repeating from the beginning of the line, until you made a complete pass through the line without making any changes. Sometimes, this took you all day, sometimes you would even make the king wait for you to finish, and that made him very angry, which is very bad.
Your sorting process is compounded by the limitation of your ability to read and compare the value of two cards, because you don't really know how to read or add, it takes you about ten seconds to decide if a card is greater than or less than another card. For a 52 card deck, this means your original sorting method would require 52 times 52 comparisons, or, in real time, seven and a half hours of non-stop work, leaving you no time to do anything else.
Over the years of doing this job for the king you discovered some shortcuts that would take less time but still produce a sorted deck, despite your limited ability to read and understand the values of the cards. While that still takes ten seconds, your new deck sorting approaches require 52 times 1.7 comparisons, or in real time, about fifteen minutes. This is much, much, better than before, but it would of course be better still if it took even less time, as you have discovered that you can now use this extra time to sort cards for all the other members of the court.
One day a traveling wizard observes you sorting cards and says "you know, this method that you have for sorting cards is quite clever but what if I were to tell you that you could sort these cards in 8.6 minutes flat?" This is about half the time it takes you right now, meaning that you could double the number of decks you sort in a day, doubling your income. You are very interested! "Tell me more," you tell the wizard. "Here, read this paper" the wizard replies, and they give you the paper GP linked.
Now, I haven't read the paper, but my guess is that the next objection is "but the king keeps switching the suits! Yesterday, they were diamonds, today they're little squiggly sketches of soft-serve poop! Which he pays me well enough to not think too hard about!" and the wizard would go on to explain that as long as you ask what the suits and numbers are in advance, you can make piles for them. Or something.
Sorry, usually I try to read papers before commenting, but I've gotta run -- I hear that whining noise that usually means my poop dispenser's jammed again.
Explaining it in further detail like you're a fellow member of our industry:
Edge computing advances are pretty important right now, since the amount of data we're working with on the edge of the network is growing very quickly. Advances in how we compute backpropagation (essentialy using a right-recursive process for the Chain Rule) mean that we can do machine learning on 2015-level phone gpus, rather than 2015-level desktop GPUs.
This advance hints at a promise of similar gains. With care, you can sort much faster. What's more, your sorts and your merge of decomposed sorts is roughly the same cost now. And multiple, layered sorts don't require custom logic or clever functional programming (at the edge developer's model) to compose.
Essentially you pick what fields in complex (maybe even nested) data structures to sort by, in what order, and the system makes a sort.
Why does this matter? Well, it will make nearly all data structures Just Faster; many of them secretly rely on ordered and sorted arrays. It makes it easier for distributed systems authors to make queryable systems (it's pretty easy to write a discriminator or specify a discriminator function remotely and then use that on the fly, as opposed to a comparator).
One place in the client-software world where it can have big impact is offline webapps. Right now, it's almost always cheaper to call out to a remote datastore rather than use a local datastore even if you're using sqlite under the covers because of the cost of synchronizing sqlite's data model. A discriminator-based approach would let you do complex queries of data in memory, but that data could still be themselves commutative data types that are coalescing data out of a data stream (or multiple data streams) remotely.
It's also worth noting that another really cool paper from this year improving on the HAMT trie (https://michael.steindorfer.name/publications/phd-thesis-eff...) could also benefit from this approach, which means all of us using immutable data structures can continue to do so with MUCH better performance characteristics but continued thread safety.
Wait, is that it? What makes this novel?
Imagine you're doing the classic "write an algorithm to determine if one word is the anagram of another". The classic solution is to sort and compare the strings to be checked.
We can do this pretty elegantly for strings without using quicksort. It goes like this: allocate a block of 26 bytes. Each byte is a counter for a letter in that position (0 -> A, 1 -> B, ...). Now sweep across the string, and each time you see a letter, increment that number. If you really care hard, you can go crazy with SIMD and other clever tricks here.
Once you're done this for 2 strings, you need only compare the two regions for equality (a constant time operation).
The worst case, average case, and minimum running time of this algorithm is O(len_word_1+len_word_2)+c, and because we can safely make assumptions about the length of the strings (no word in anym ISO-Latin-1 encoding exceeds 255 of any character), we can do it fairly compactly. This is MUCH faster and can be done with perfect memory safety (which is to say, it is optimal with mutability but all operations are commutative so they may be subdivided, done in any order, and recombined at will).
Try implementing this. It's really, really fast on modern hardware. Especially if you're working it out for a full /usr/share/dict/words file on a normal _nix/bsd installation.
We can even see that composability feature in our current problem! Imagine we have have that big dictionary file and we want to find ALL the anagrams in it. Imagine we start cutting the above technique in half and computing the byte vector for every word in the dictionary (which is O(n) time). If we sort THESE vectors, we could just pick out all the equal values and say "There are the anagram groups", right?
If we were to insert them into some very wide trie (as is the style these days), that'd be O(Numwords * log(numwords)), which is really just sorting again. We'd do it in minimum 2 passes with O(n log n) cost. But if we have composable discriminators we could do this in one big pass with O(n) cost! Our constant factors would grow because we're dealing with more memory. We could then not only sweep across the sorted list to extract groups (note the symmetry with the ABC counter approach above), but we could also pretend it's a tree structure (start at the length/2 element in the list and pretend its a binary tree) and search for new items inside the block, so we could check for new word anagrams subsequently in O(log n) time.
The paper I linked talked about generalizing this idea (a special case of radix sort) and making it composable.
As collected by the SIGBOVIK group:
"Following the popularity of MapReduce, a whole ecosystem
of Apache Incubator Projects has emerged that all solve the
same problem. Famous examples include Apache Hadoop,
Apache Spark, Apache Pikachu, Apache Pig, German Spark
and Apache Hive . However, these have proven to be
unusable because they require the user to write code in Java.
Another solution to distributed programming has been
proposed by Microsoft with their innovative Excel system. In
large companies, distributed execution can be achieved using
Microsoft Excel by having hundreds of people all sitting on
their own machine working with Excel spreadsheets. These
hundreds of people e combined can easily do the work of a
single database server."
PS: This thread is great, i'm bookmarking because here there are good (serious) papers.
C. Shannon, "A Symbolic Analysis of Relay and Switching Circuits" (1940): https://dspace.mit.edu/bitstream/handle/1721.1/11173/3454142...
Shannon's master's thesis, which introduces boolean algebra to the field of digital circuit design.
R.W. Hamming, "Error Detecting and Error Correcting Codes" (1950): https://ia801903.us.archive.org/1/items/bstj29-2-147/bstj29-...
In Hamming's own words: "Damn it, if the machine can detect an error, why can't it locate the position of the error and correct it?"
J.T. Kajiya, "The Rendering Equation" (1986):
Kajiya introduces the integral rendering equation, which is the basis for most current techniques of physically based rendering.
I know Thompson's "Reflections on Trust" and Shannon's "Communication" papers are more famous but I believe BCS's "Correctness" paper has more immediate relevance to a wider population of programmers.
For example, I don't believe Ethereum's creator, Vitalik Buterin, is familiar with it because if he was, he would have realized that "code is law" is not possible and therefore he would have predicted the DAO hack and subsequent fork/reversal to undo the code.
Seriously, if you read BCS's paper and generalize its lessons learned, you will see that the DAO hack and its reversal as inevitable.
I think for many applications there is no binary answer, it's not just a good or bad idea. The question is how good can we get and is it any better than the state of the art? There are theoretical limits, but the interesting part is whether there exists a practical approximation. I don't believe in a fundamental difference between us and computers, i think everything we can reason about should be possible to algorithmically reason about. I think smart-contracts are a fundamental improvement over "non-code as law", i really believe in them. They are reproducible and exact. But it's a shame that solidity is so badly engineered, because they it is really hard to prove anything in it. I think they did the exact opposite of what would be the right language. I understand the reasoning behind "the limits of correctness", but does this means that proving anything is meaningless?
I would expect most contracts to be stupidly simpel, at least to a machine, with simpel properties that need to be proven comparable to testing Haskell with quickcheck. And i believe they are an improvement over "non-code as law", even if not provably correct.
The problem with bugs and smart-contracts is interesting. But implementing smart-contracts does not mean automating the judge.
Honest naïve question. What's the proof?
But, for example, people have also lost money due to bugs in the Solidity compiler: https://np.reddit.com/r/ethtrader/comments/5foa5p/daily_disc...
How many "more careful" Ethereum programmers also check the compiler for correctness?
Another paper in this vein is James Fetzer's "Program Verification: The Very Idea". http://lore.ua.ac.be/Teaching/SSPEC2LIC/critique2.pdf
But -- and this is the crucial point -- that doesn't mean we shouldn't strive to be better than we are now.
The impossibility of perfectly modeling the world hasn't prevented us from making enormous progress on software safety and security over the past 30 odd years. Today, if you care to, you can easily write code that is free of buffer overflows and command injection attacks. In the 00's SQL injection attacks were extremely easy to find; now they're comparatively rare.
Smith's paper tells us that code-as-law is probably a bad idea. But it is not -- and wasn't intended to be -- an indict of static analysis or model-based engineering more generally. Every structural engineer knows the difference between a bridge and a model of a bridge; a paper pointing out the difference without substantively critiquing the practical usefulness of a particular type of model would probably elicit eye-rolls. I'm not sure why these mundane observations receive such attention in computer science. Maybe because with software the model and the system look so similar.
But to be sure, the impossibility of codifying human morality is a pretty lame excuse for failing to use static analysis tools or better languages or quality frameworks to prevent known classes of attacks. So I doubt that's what Smith is advocating.
> How many "more careful" Ethereum programmers also check the compiler for correctness?
Yes, we should obviously check compilers for correctness, and we're making slow but sure progress toward a world where our critical infrastructure comes with strong --
of course, never perfect -- correctness guarantees.
Agreed! And I also agree that we are really making progress. But we're far from a world where people can crank out provably correct code (let alone the proofs).
> I'm not sure why these mundane observations receive such attention in computer science. Maybe because with software the model and the system look so similar.
Excellent point, and yes, I think that is the problem. Modeling the world in code is not much different from... er... modeling the world in code :-)
The programmer only has to comply with the specification. The specification is a finite syntactic entity. If the specification doesn't capture what the user really wants, or compliance is undecidable, or <insert problem beyond the programmer's control here>, then the one at fault is the specification writer, not the programmer.
Can programming be liberated from the von Neumann style? - John Backus's Turing lecture - http://dl.acm.org/citation.cfm?id=1283933
 - http://www-formal.stanford.edu/jmc/recursive.html
Where Turing's paper put us on the journey towards AI, 57 years later Dreyfus points out how the direction chosen is not only wrong, but hopelessly misguided.
As I studied the RAND papers and memos, I found to my surprise that, far from replacing philosophy, the pioneers in CS and AI had learned a lot, directly and indirectly from the philosophers. They had taken over Hobbes’ claim that reasoning was calculating, Descartes' mental representations, Leibniz's idea of a "universal characteristic" - a set of primitives in which all knowledge could be expressed, -- Kant’s claim that concepts were rules, Frege's formalization of such rules, and Wittgenstein's postulation of logical atoms in his Tractatus. In short, without realizing it, AI researchers were hard at work turning rationalist philosophy into a research program.
It concerns randomized algorithms, which are algorithms that try to overcome worst case performance by randomizing their behavior, so that a malicious user can't know which input will be the worst case input this time.
The principle states that the expected cost of a randomized algorithm on a single input is no better or worse than the cost of a deterministic algorithm with random input.
Yao proves this is the case by constructing two zero sum games based around the algorithms' running times and then using game theory (specifically von Neumann's minimax theorem) to show that the two approaches are equivalent. It's a really neat approach!
Not only for the historical value of changing the world, and for the fact that it's very interesting and readable; It has personal value to me: the first CS paper I've ever read and it inspired me and changed the course of my life, literally.
Also, it has some very amusingly naive (in hindsight) stuff in it, like: "Google does not have any optimizations such as query caching, subindices on common terms, and other common optimizations. We intend to speed up Google considerably through distribution and hardware, software, and algorithmic improvements. Our target is to be able to handle several hundred queries per second"
State in Haskell. John Launchbury and Simon L. Peyton Jones
it's a book though: https://www.amazon.com/Sparse-Distributed-Memory-MIT-Press/d...
I read it multiple times and still don't quite understand it all.
There are more great papers i read but this one comes back to mind more often then others.
This paper, written during WW II (!) by someone who had around to 20 years of computing experience at that time (!!) introduced the world to the ideas like hypertext, and citation indexes. Google's PageRank algorithm can be seen as a recombining of ideas from this paper.
This is worth reading to see how much was understood how early.
Programming with Agents: http://alumni.media.mit.edu/~mt/thesis/mt-thesis-Contents.ht...
Here is a short paper with a clear description of an ingenious idea.
Engineered Robustness by Controlled Hallucination: http://web.mit.edu/jakebeal/www/Publications/NIAI-2008.pdf
I like the simplicity of it. Most CS researches seem to be afraid of describing things that are simple, even if those things are non-obviosu and valuable.
No Silver Bullet, by Fred Brooks http://worrydream.com/refs/Brooks-NoSilverBullet.pdf
The original STL documentation https://www.sgi.com/tech/stl/table_of_contents.html
That it still holds true almost 50 years later is pretty amazing.
Jeffrey Ullman & John Hopcroft:
Formal languages and their relation to automata 
Ted Codd: A relational model of data for large shared data banks 
C.A.R Hoare: Communicating Sequential Processes 
Full tail recursion scheme implementation by never "return" in C
> Basically, you’re either dealing with Mossad or not-Mossad. If your adversary is not-Mossad, then you’ll probably be fine if you pick a good password and don’t respond to emails from ChEaPestPAiNPi11s@ virus-basket.biz.ru. If your adversary is the Mossad, YOU’RE GONNA DIE AND THERE’S NOTHING THAT YOU CAN DO ABOUT IT
More security researchers need to learn about that.
Which documents the invention of rsync, it's a good read.
AlphaGo learning from self-play? Tridgell did it in 1998.
Weirdly, the paper isn't much discussed alongside the usual contenders. (WOOT, Logoot, RGA, LSEQ, etc.)
I think this paper is very cute and also technically interesting.
James W. Cooley and John W. Tukey
Mathematics of Computation
Vol. 19, No. 90 (Apr., 1965), pp. 297-301
As a follow-up, let me recommend Püschel & Moura (2006) “Algebraic Signal Processing Theory”, https://arxiv.org/pdf/cs/0612077.pdf and Püschel’s other papers about similar topics.
Or in a different direction, DJB (2008), “Fast multiplication and its applications”, http://cr.yp.to/lineartime/multapps-20080515.pdf
I'm not sure if it was the fact that I was just a kid when I read it, but it was just so obvious and simple but so complicated and amazing at the same time.
"Copycat is a model of analogy making and human cognition based on the concept of the parallel terraced scan, developed in 1988 by Douglas Hofstadter, Melanie Mitchell, and others at the Center for Research on Concepts and Cognition, Indiana University Bloomington. Copycat produces answers to such problems as "abc is to abd as ijk is to what?" (abc:abd :: ijk:?). Hofstadter and Mitchell consider analogy making as the core of high-level cognition, or high-level perception, as Hofstadter calls it, basic to recognition and categorization. High-level perception emerges from the spreading activity of many independent processes, called codelets, running in parallel, competing or cooperating. They create and destroy temporary perceptual constructs, probabilistically trying out variations to eventually produce an answer. The codelets rely on an associative network, slipnet, built on pre-programmed concepts and their associations (a long-term memory). The changing activation levels of the concepts make a conceptual overlap with neighboring concepts." -- https://en.wikipedia.org/wiki/Copycat_(software)
I'll be interested to see where/(if) it gets published.
... a completely lock-free operating system optimized using run-time code generation, written from scratch in assembly running on a homemade two-CPU SMP with a two-word compare-and-swap instruction — you know, nothing fancy.
Which (necessarily) undersells by a very large margin just how impressive, innovative, and interesting this thesis is.
If you’re interested in operating systems, or compilers, or concurrency, or data structures, or real-time programming, or benchmarking, or optimization, you should read this thesis. Twenty-five years after it was published, it still provides a wealth of general inspiration and specific food for thought. It’s also clearly and elegantly written. And, as a final bonus, it’s a snapshot from an era in which Sony made workstations and shipped its own, proprietary, version of Unix. Good times.
'The Geometry of Innocent Flesh on the Bone: Return-into-libc without function calls' by Hovav Shacham
1) 1990 http://cife.stanford.edu/node/599
2) 1993 http://faculty.cs.tamu.edu/ioerger/cs631-fall05/AOP.pdf
Why it was important: you can't build big complex systems without these principles.
Some people say he was instrumental in stopping the Star Wars program, he argued it would be impossible to test outside of war (and therefore doomed).
Immediately useful for anyone measuring compiler transformations performance!
 http://plasma.cs.umass.edu/emery/stabilizer.html "Stabilizer: Statistically Sound Performance Evaluation" by Charlie Curtsinger and Emery Berger, ASPLOS 2013
This paper develops a precise model for internal iteration of a data structure, such that exactly the necessary information is exposed and no more.
It's a fantastic exploration of improving a well-known design space with justified removal of details. I keep its lessons in mind whenever I am facing code that seems to have a lot of incidental complexity.
I read it first as a normal CS paper, but later started seeing it as a commentary on an extremely busy work life.
Right there in the first paragraph: "... receive livelock, in which the system spends all its time processing interrupts, to the exclusion of other tasks..."
Does this remind you of anything?
The article provides a safety checklist for use during the analysis of software requirements for spacecraft and other safety-critical, embedded systems.
This shows how you end up "differentiating" datatypes in the context of strict functional programming, in order to do things like "mutate" lists. It is essentially the same as what mathematicians call "combinatorial species".
Human-level control through deep reinforcement learning
It's not world-changing or even particularly novel, but it's such a simple concept explained very well that really changes how you see the typed/dynamic language divide, as well as language design in general.
"On the Translation of Languages from Left to Right", by Knuth, I found much clearer and more illuminating than any of the secondary literature on LR(k) parsing.
This paper explains a beautiful algorithm for matching regular expressions with a Socratic dialogue.
Focusing on the flow of data between operators and greedily executing a linear program is what an out-of-order processor is.
As an architectural lighting guy, seeing realtime global illumination look this good in a game engine was fantastic. Parts of the algorithm I can understand, parts go over my head still, but the results are amazing.
A big part of what I do at work is radiosity simulations in AGI32 which is of course more accurate (because it's trying to accurately simulate real world lighting results) but much much slower.
http://research.nvidia.com/publication/interactive-indirect-... has the paper plus presentation available, plus abstract and other links.
I got the citeseerx.ist.psu.edu link off google and it worked for me the first time. I'm an alum but not logged into anything, so either it was a bypass via google referrer (not sure why they'd do that, it's not looking for advertising clicks) or some usage limit.
I'll need to go with
Gilbert, E., & Karahalios, K. (2009, April). Predicting tie strength with social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 211-220). ACM.
In grad school, it was the paper that kept on giving. I think I cited it every semester for a paper or project. There's a lot of other papers and books that really inspired me, but this one was magic.
It's just such a cool result and the paper is very well written. Further, the dynamic optimality conjecture at the end is still an open problem.
The most simple and most effective hash table scheme, and nobody is using it, or even knows about it. Fastest and least memory, but not thread-safe. After 12 years there's still nothing better on the horizon.
Though the CheneyMTA paper is also brilliant, a typical Baker paper.
Proof that the busy beaver function is not computable.
One of the best practical "How can this improve our business?" technical papers, and an excellent introduction to reading papers.
Finding the k Shortest Paths by D. Eppstein
Great writing style!
It's not technically a CS paper, but well worth the (very short) read regardless.
Some of the others that have already been mentioned on this thread:
- Time, Clocks, and the Ordering of Events in a Distributed System
- Paxos Made Simple
One might argue this is not CS, but it's something everyone should read and understand.
Homomorphic Encryption over the Integers
Growing a Language by Guy Steele (co-inventory of Scheme). Brilliant speech about how to grow languages and why it's necessary. Languages that can be grown by the programmer, like Lisp or Smalltalk are better than languages that fixed like most others, this is why.